How Jobefi's AI Fit-Scoring Actually Works

Why spray-and-pray job applications don't work

Spray-and-pray means applying to every job you're roughly qualified for, as fast as possible, with a resume that barely changes between roles. It doesn't work because it optimizes for the wrong thing: volume instead of fit. Every recruiter I've talked to says the same thing - a generic application is easy to spot and easy to reject.

Auto-apply tools made this worse, not better. They turned a bad habit into a bot that can do it a thousand times faster. The candidate doesn't get more interviews - they get more silence, plus a pile of applications they can't account for if a recruiter ever calls back.

Jobefi is built on the opposite bet: fewer applications, each one actually fit for the role, each one you can explain in an interview because you reviewed it yourself before it went out.

How fit-scoring actually works

Jobefi scores a job posting against your CV and onboarding profile, then gives you a 0-100 fit score with a plain-language explanation. Nothing gets applied to until that score clears the threshold you set.

What Jobefi reads from your CV

Jobefi reads your uploaded CV plus the structured answers from your 16-step onboarding: target roles, experience history, work-mode preference, salary range, industries, and availability.

The onboarding answers matter as much as the CV text. A CV alone doesn't tell you someone won't relocate, or needs visa sponsorship, or is only looking at remote roles - so Jobefi asks for that directly instead of guessing.

What Jobefi reads from a job posting

Jobefi reads the full posting text pulled directly from the source ATS - Greenhouse, Ashby, or Lever - through a per-source adapter built and verified against that source's real API response. Nothing is scraped from a search snippet or guessed from a job title.

Each source has its own field-mapping config, because job boards don't agree on what a field is called. Getting this wrong silently would mean scoring garbage data, so every adapter is checked against a real posting before it ships.

The three-part sub-score breakdown

The fit score breaks into three sub-scores instead of one opaque number, so you can see which part of the match is strong and which part is a stretch.

Every job card and evaluate view shows the same fit ring and sub-score bars, so the breakdown looks and behaves identically whether you're scanning a feed or reading one posting in detail.

Why every match comes with a plain-language explanation

Every fit score ships with a plain-language explanation of why it matched, because a number without a reason isn't something you can trust or act on. You should be able to read the explanation and immediately agree or disagree with it.

This is the same reason Jobefi logs an audit-log row for every automated action it takes. If you're ever asked in an interview why you applied to a role, the honest answer should be sitting right there, not reconstructed from memory.

You review and submit - Jobefi never applies without you

Jobefi prepares - it tailors your resume and cover letter, drafts answers to free-text ATS questions - but a human reviews and submits every application. It is never fully autonomous.

This isn't a limitation I'm planning to remove later. Autonomous submission is a trust problem, not just a technical one - I don't think a tool should be able to put your name on a document you haven't seen. The AI-drafted ATS answers work the same way: drafted for you, marked review-required, never sent on their own.

Same principle applies to anything Jobefi infers later, like a rejection or interview outcome detected from your inbox - it will suggest a status change for you to confirm, never flip it silently.

What happens after you mark an application as submitted

Once you submit, you mark the application as submitted with one click and it moves into your pipeline, where you can track it against every other application you've sent. That one click is also available directly from the browser extension overlay on the job posting itself.

Tracking what happens after submission - automatically, from a rejection or interview email - is designed but not built yet. Today, moving an application forward in the pipeline is a manual step, and I'd rather say that plainly than imply it's automatic before it is.

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